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Getting started with FastApi

Author : Andrés Cruz Yoris
Publisher : Andres Cruz
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4./5 ( download)

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Book Synopsis Getting started with FastApi by : Andrés Cruz Yoris

Download or read book Getting started with FastApi written by Andrés Cruz Yoris and published by Andres Cruz. This book was released on with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: FastAPI is a great web framework for creating web APIs with Python; It offers us multiple features with which it is possible to create modular, well-structured, scalable APIs with many options such as validations, formats, typing, among others. When you install FastAPI, two very important modules are installed: Pydantic that allows the creation of models for data validation. Starlette, which is a lightweight ASGI tooltip, used to create asynchronous (or synchronous) web services in Python. With these packages, we have the basics to create APIs, but we can easily extend a FastAPI project with other modules to provide the application with more features, such as the database, template engines, among others. FastAPI is a high-performance, easy-to-learn, start-up framework; It is ideal for creating all kinds of sites that not only consist of APIs, but we can install a template manager to return complete web pages. This book is mostly practical, we will learn the basics of FastAPI, knowing its main features based on a small application that we will extend chapter after chapter and whose content you can see below: Chapter 1: We present some essential commands to develop in FastApi , we will prepare the environment and we will give an introduction to the framework . Chapter 2: One of the main factors in FastApi is the creation of resources for the API through functions, in this section we will deal with the basics of this, introducing routing between multiple files as well as the different options for the arguments and parameters of these routes. Chapter 3: In this section, learn how to handle HTTP status codes from API methods and also handle errors/exceptions from API methods. Chapter 4: In this section we will see how to create sample data to use from the automatic documentation that FastAPI offers for each of the API methods. Chapter 5: In this chapter we will see how to implement the upload of files, knowing the different existing variants in FastAPI. Chapter 6: In this chapter we will see how to connect a FastAPI application to a relational database such as MySQL. Chapter 7: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 8: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 9: In this chapter we will learn how to use dependencies. Chapter 10: In this chapter we will see how to use middleware to intercept requests to API methods and execute some procedure before the request or after generating the response. Chapter 11: In this chapter we will see how to create a user module, to register users, login, generate access tokens and logout. Chapter 12: In this chapter we will learn about some particularities and functionalities of FastAPI such as the use of annotations and the Ellipsis operator. Chapter 13: In this chapter we will see how to implement unit tests. Chapter 14: In this chapter we will know some general aspects applied to FastAPI.

Building Python Web APIs with FastAPI

Author : Abdulazeez Abdulazeez Adeshina
Publisher : Packt Publishing Ltd
ISBN 13 : 1801074518
Total Pages : 216 pages
Book Rating : 4.8/5 (1 download)

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Book Synopsis Building Python Web APIs with FastAPI by : Abdulazeez Abdulazeez Adeshina

Download or read book Building Python Web APIs with FastAPI written by Abdulazeez Abdulazeez Adeshina and published by Packt Publishing Ltd. This book was released on 2022-07-29 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover FastAPI features and best practices for building and deploying high-quality web APIs from scratch Key Features • A practical guide to developing production-ready web APIs rapidly in Python • Learn how to put FastAPI into practice by implementing it in real-world scenarios • Explore FastAPI, its syntax, and configurations for deploying applications Book Description RESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code. This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You'll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment. By the end of this book, you'll have developed a solid understanding of the FastAPI framework and be able to build and deploy robust REST APIs. What you will learn • Set up a FastAPI application that is fully functional and secure • Understand how to handle errors from requests and send proper responses in FastAPI • Integrate and connect your application to a SQL and NoSQL (MongoDB) database • Perform CRUD operations using SQL and FastAPI • Manage concurrency in FastAPI applications • Implement authentication in a FastAPI application • Deploy a FastAPI application to any platform Who this book is for This book is for Python developers who want to learn FastAPI in a pragmatic way to create robust web APIs with ease. If you are a Django or Flask developer looking to try something new that's faster, more efficient, and produces fewer bugs, this FastAPI Python book is for you. The book assumes intermediate-level knowledge of Python programming.

Microservice APIs

Author : Jose Haro Peralta
Publisher : Simon and Schuster
ISBN 13 : 1638350566
Total Pages : 438 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Microservice APIs by : Jose Haro Peralta

Download or read book Microservice APIs written by Jose Haro Peralta and published by Simon and Schuster. This book was released on 2023-03-07 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Strategies, best practices, and patterns that will help you design resilient microservices architecture and streamline your API integrations. In Microservice APIs, you’ll discover: Service decomposition strategies for microservices Documentation-driven development for APIs Best practices for designing REST and GraphQL APIs Documenting REST APIs with the OpenAPI specification (formerly Swagger) Documenting GraphQL APIs using the Schema Definition Language Building microservices APIs with Flask, FastAPI, Ariadne, and other frameworks Service implementation patterns for loosely coupled services Property-based testing to validate your APIs, and using automated API testing frameworks like schemathesis and Dredd Adding authentication and authorization to your microservice APIs using OAuth and OpenID Connect (OIDC) Deploying and operating microservices in AWS with Docker and Kubernetes Microservice APIs teaches you practical techniques for designing robust microservices with APIs that are easy to understand, consume, and maintain. You’ll benefit from author José Haro Peralta’s years of experience experimenting with microservices architecture, dodging pitfalls and learning from mistakes he’s made. Inside you’ll find strategies for delivering successful API integrations, implementing services with clear boundaries, managing cloud deployments, and handling microservices security. Written in a framework-agnostic manner, its universal principles can easily be applied to your favorite stack and toolset. About the technology Clean, clear APIs are essential to the success of microservice applications. Well-designed APIs enable reliable integrations between services and help simplify maintenance, scaling, and redesigns. Th is book teaches you the patterns, protocols, and strategies you need to design, build, and deploy effective REST and GraphQL microservices APIs. About the book Microservice APIs gathers proven techniques for creating and building easy-to-consume APIs for microservices applications. Rich with proven advice and Python-based examples, this practical book focuses on implementation over philosophy. You’ll learn how to build robust microservice APIs, test and protect them, and deploy them to the cloud following principles and patterns that work in any language. What's inside Service decomposition strategies for microservices Best practices for designing and building REST and GraphQL APIs Service implementation patterns for loosely coupled components API authorization with OAuth and OIDC Deployments with AWS and Kubernetes About the reader For developers familiar with the basics of web development. Examples are in Python. About the author José Haro Peralta is a consultant, author, and instructor. He’s also the founder of microapis.io. Table of Contents PART 1 INTRODUCING MICROSERVICE APIS 1 What are microservice APIs? 2 A basic API implementation 3 Designing microservices PART 2 DESIGNING AND BUILDING REST APIS 4 Principles of REST API design 5 Documenting REST APIs with OpenAPI 6 Building REST APIs with Python 7 Service implementation patterns for microservices PART 3 DESIGNING AND BUILDING GRAPHQL APIS 8 Designing GraphQL APIs 9 Consuming GraphQL APIs 10 Building GraphQL APIs with Python PART 4 SECURING, TESTING, AND DEPLOYING MICROSERVICE APIS 11 API authorization and authentication 12 Testing and validating APIs 13 Dockerizing microservice APIs 14 Deploying microservice APIs with Kubernetes

Python APIs

Author : Robert Johnson
Publisher : HiTeX Press
ISBN 13 :
Total Pages : 306 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Python APIs by : Robert Johnson

Download or read book Python APIs written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Python APIs: From Concept to Implementation" is an essential guide for anyone seeking to master the art of API development using Python. This comprehensive resource covers the fundamental concepts of APIs, unraveling their architecture, protocols, and real-world applications. With a clear focus on RESTful APIs, this book explores the design principles and best practices required to build intuitive and scalable APIs. From selecting the right data formats to implementing robust security measures, the book provides detailed insights that cater to the evolving needs of modern software development. Delving into practical applications, the book offers step-by-step guidance on utilizing popular Python frameworks like Flask and FastAPI to construct efficient APIs. Readers are taken through the entire lifecycle of API development, from documentation and testing to deployment and scaling. The inclusion of advanced topics such as asynchronous programming, integration strategies, and performance optimization ensures a comprehensive understanding. All aspects of API development are explored to prepare readers for the challenges of integrating APIs into dynamic applications and scaling them to handle increased demand. "Python APIs: From Concept to Implementation" equips beginners and seasoned developers alike with the knowledge and tools needed to create powerful, reliable, and secure APIs using Python's versatile capabilities. Whether you're building simple applications or managing complex enterprise systems, this book is your ultimate companion in achieving robust API solutions.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Learn Python From an Expert: The Complete Guide: With Artificial Intelligence

Author : Edson L P Camacho
Publisher :
ISBN 13 :
Total Pages : 620 pages
Book Rating : 4.3/5 (976 download)

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Book Synopsis Learn Python From an Expert: The Complete Guide: With Artificial Intelligence by : Edson L P Camacho

Download or read book Learn Python From an Expert: The Complete Guide: With Artificial Intelligence written by Edson L P Camacho and published by . This book was released on 2023-06-08 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Ultimate Guide to Advanced Python and Artificial Intelligence: Unleash the Power of Code! Are you ready to take your Python programming skills to the next level and dive into the exciting world of artificial intelligence? Look no further! We proudly present the comprehensive book written by renowned author Edson L P Camacho: "Advanced Python: Mastering AI." In today's rapidly evolving technological landscape, the demand for AI professionals is soaring. Python, with its simplicity and versatility, has become the go-to language for AI development. Whether you are a seasoned Pythonista or a beginner eager to learn, this book is your gateway to mastering AI concepts and enhancing your programming expertise. What sets "Advanced Python: Mastering AI" apart from other books is its unparalleled combination of in-depth theory and hands-on practicality. Edson L P Camacho, a leading expert in the field, guides you through every step, from laying the foundation of Python fundamentals to implementing cutting-edge AI algorithms. Here's a glimpse of what you'll find within the pages of this comprehensive guide: 1. Python Fundamentals: Review and reinforce your knowledge of Python basics, including data types, control flow, functions, and object-oriented programming. Build a solid foundation to tackle complex AI concepts. 2. Data Manipulation and Visualization: Learn powerful libraries such as NumPy, Pandas, and Matplotlib to handle and analyze data. Understand how to preprocess and visualize data effectively for AI applications. 3. Machine Learning Essentials: Dive into the world of machine learning and explore popular algorithms like linear regression, decision trees, support vector machines, and neural networks. Discover how to train, evaluate, and optimize models for various tasks. 4. Deep Learning and Neural Networks: Delve deeper into neural networks, the backbone of modern AI. Gain insights into deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Implement advanced techniques like transfer learning and generative models. 5. Natural Language Processing (NLP): Explore the fascinating field of NLP and learn how to process and analyze textual data using Python. Discover techniques like sentiment analysis, named entity recognition, and text generation. 6. Computer Vision: Unleash the power of Python for image and video analysis. Build computer vision applications using popular libraries like OpenCV and TensorFlow. Understand concepts like object detection, image segmentation, and image captioning. 7. Reinforcement Learning: Embark on the exciting journey of reinforcement learning. Master the fundamentals of Q-learning, policy gradients, and deep Q-networks. Create intelligent agents that can learn and make decisions in dynamic environments. "Advanced Python: Mastering AI" not only equips you with the theoretical knowledge but also provides numerous real-world examples and projects to reinforce your understanding. Each chapter is accompanied by practical exercises and coding challenges to sharpen your skills and boost your confidence. Don't miss the opportunity to stay ahead in this AI-driven era. Order your copy of "Advanced Python: Mastering AI" today and unlock the full potential of Python programming with artificial intelligence. Take your career to new heights and become a proficient AI developer. Get ready to write the code that shapes the future!

MANUAL OF PYTHON FOR WEB DEVELOPMENT 2024 Edition

Author : Diego Rodrigues
Publisher : Diego Rodrigues
ISBN 13 :
Total Pages : 153 pages
Book Rating : 4./5 ( download)

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Book Synopsis MANUAL OF PYTHON FOR WEB DEVELOPMENT 2024 Edition by : Diego Rodrigues

Download or read book MANUAL OF PYTHON FOR WEB DEVELOPMENT 2024 Edition written by Diego Rodrigues and published by Diego Rodrigues. This book was released on 2024-11-03 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Python for Web Development: Modern Frameworks and Tools". This book is an essential guide for students, professionals, and managers who want to master the technologies shaping the future of web development. Written by Diego Rodrigues, one of the world's leading technical book authors with over 180 titles published in five languages, this manual offers a comprehensive and practical approach to web development with Python. Covering everything from the basics to the most advanced practices, this book provides quick and effective learning using advanced tech writing and storytelling techniques. You will find clear theories, practical examples, case studies, and tools that will facilitate the immediate application of the acquired knowledge. Whether you are beginning your journey into the world of web development or looking to enhance your skills, this book has been carefully structured to meet your needs and exceed expectations. Each chapter has been designed to be a fundamental piece in your understanding of these technologies, ensuring that you are prepared to face challenges and seize the opportunities that the future holds. Open the sample of this book and discover how web development with Python can transform your practices, bringing innovation, efficiency, and strategic vision to your projects and business. Tags Python Django Flask FastAPI web development frameworks design backend frontend APIs RESTful GraphQL agile DevOps test automation pytest CI/CD Git GitHub Docker Kubernetes web security authentication JWT OAuth encryption SQLAlchemy ORM templates HTML CSS JavaScript React Vue.js Angular build tools Webpack continuous integration cloud deployment AWS Heroku DigitalOcean Azure cloud computing scalability load balancing microservices containers virtualization server Nginx Apache performance optimization monitoring logging debugging documentation Swagger ReDoc OpenAPI JSON Schema type hints asyncio ASGI virtual environments pip venv poetry code quality flake8 black code formatting test coverage case studies practical examples best practices innovation efficiency digital transformation Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques cybersecurity skills cybersecurity industry global cybersecurity trends Kali Linux tools cybersecurity education cybersecurity innovation penetration test tools cybersecurity best practices global cybersecurity companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle cybersecurity consulting cybersecurity framework network security cybersecurity courses cybersecurity tutorials Linux security cybersecurity challenges cybersecurity landscape cloud security cybersecurity threats cybersecurity compliance cybersecurity research cybersecurity technology

Data Engineering for Machine Learning Pipelines

Author : Pavan Kumar Narayanan
Publisher : Springer Nature
ISBN 13 :
Total Pages : 651 pages
Book Rating : 4.8/5 (688 download)

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Book Synopsis Data Engineering for Machine Learning Pipelines by : Pavan Kumar Narayanan

Download or read book Data Engineering for Machine Learning Pipelines written by Pavan Kumar Narayanan and published by Springer Nature. This book was released on with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Introducing Python

Author : Bill Lubanovic
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492051322
Total Pages : 634 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Introducing Python by : Bill Lubanovic

Download or read book Introducing Python written by Bill Lubanovic and published by "O'Reilly Media, Inc.". This book was released on 2019-11-06 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages.

Learn Python by Building Data Science Applications

Author : Philipp Kats
Publisher : Packt Publishing Ltd
ISBN 13 : 1789533066
Total Pages : 464 pages
Book Rating : 4.7/5 (895 download)

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Book Synopsis Learn Python by Building Data Science Applications by : Philipp Kats

Download or read book Learn Python by Building Data Science Applications written by Philipp Kats and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand the constructs of the Python programming language and use them to build data science projects Key FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook Description Python is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production. This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice. By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards. What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is for If you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.

Python for AI

Author : Robert Johnson
Publisher : HiTeX Press
ISBN 13 :
Total Pages : 414 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Python for AI by : Robert Johnson

Download or read book Python for AI written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-23 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Python for AI: Applying Machine Learning in Everyday Projects" is a comprehensive guide designed for anyone keen to delve into the transformative world of artificial intelligence using the potent yet accessible Python programming language. This book meticulously covers essential AI concepts, offering readers a structured path from understanding basic Python syntax to implementing sophisticated machine learning models. With a blend of foundational theories and practical applications, each chapter deftly guides readers through relevant techniques and tools, such as TensorFlow, Keras, and scikit-learn, that are crucial for modern AI development. Whether you are a beginner taking your first steps into AI or someone with programming experience seeking to expand your skill set, this book ensures you are equipped with the knowledge needed to tackle real-world challenges. It goes beyond mere theory, providing insights into deploying and integrating AI models, handling large datasets, and effectively developing solutions applicable across various industries. By the end of this journey, readers will not only grasp the intricacies of AI projects but also gain the confidence to innovate and contribute significantly to the evolving landscape of artificial intelligence.

A Hands-On Introduction to Essential Python Libraries and Frameworks (With Code Samples)

Author : Murat Durmus
Publisher : Murat Durmus
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4./5 ( download)

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Book Synopsis A Hands-On Introduction to Essential Python Libraries and Frameworks (With Code Samples) by : Murat Durmus

Download or read book A Hands-On Introduction to Essential Python Libraries and Frameworks (With Code Samples) written by Murat Durmus and published by Murat Durmus. This book was released on 2023-03-02 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Python libraries and frameworks that every aspiring data scientist, ML engineer, and Python developer should know. "Python is not just a language, it's a community where developers can learn, collaborate and create wonders." ~ Guido van Rossum (Creator of Python)

Applied Machine Learning Solutions with Python

Author : Siddhanta Bhatta
Publisher : BPB Publications
ISBN 13 : 9391030432
Total Pages : 418 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Applied Machine Learning Solutions with Python by : Siddhanta Bhatta

Download or read book Applied Machine Learning Solutions with Python written by Siddhanta Bhatta and published by BPB Publications. This book was released on 2021-08-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: A problem-focused guide for tackling industrial machine learning issues with methods and frameworks chosen by experts. KEY FEATURES ● Popular techniques for problem formulation, data collection, and data cleaning in machine learning. ● Comprehensive and useful machine learning tools such as MLFlow, Streamlit, and many more. ● Covers numerous machine learning libraries, including Tensorflow, FastAI, Scikit-Learn, Pandas, and Numpy. DESCRIPTION This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies. The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API. Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets. WHAT YOU WILL LEARN ● Construct a machine learning problem, evaluate the feasibility, and gather and clean data. ● Learn to explore data first, select, and train machine learning models. ● Fine-tune the chosen model, deploy, and monitor it in production. ● Discover popular models for data analytics, computer vision, and Natural Language Processing. ● Create a machine learning profile and contribute to the community. WHO THIS BOOK IS FOR This book caters to beginners in machine learning, software engineers, and students who want to gain a good understanding of machine learning concepts and create production-ready ML systems. This book assumes you have a beginner-level understanding of Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Problem Formulation in Machine Learning 3. Data Acquisition and Cleaning 4. Exploratory Data Analysis 5. Model Building and Tuning 6. Taking Our Model into Production 7. Data Analytics Use Case 8. Building a Custom Image Classifier from Scratch 9. Building a News Summarization App Using Transformers 10. Multiple Inputs and Multiple Output Models 11. Contributing to the Community 12. Creating Your Project 13. Crash Course in Numpy, Matplotlib, and Pandas 14. Crash Course in Linear Algebra and Statistics 15. Crash Course in FastAPI

Microservices for Machine Learning

Author : Rohit Ranjan
Publisher : BPB Publications
ISBN 13 : 9355516886
Total Pages : 480 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Microservices for Machine Learning by : Rohit Ranjan

Download or read book Microservices for Machine Learning written by Rohit Ranjan and published by BPB Publications. This book was released on 2024-04-20 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empowering AI innovations: The fusion of microservices and ML KEY FEATURES ● Microservices and ML fundamentals, advancements, and practical applications in various industries. ● Simplify complex ML development with distributed and scalable microservices architectures. ● Discover real-world scenarios illustrating the fusion of microservices and ML, showcasing AI's impact across industries. DESCRIPTION Explore the link between microservices and ML in Microservices for Machine Learning. Through this book, you will learn to build scalable systems by understanding modular software construction principles. You will also discover ML algorithms and tools like TensorFlow and PyTorch for developing advanced models. It equips you with the technical know-how to design, implement, and manage high-performance ML applications using microservices architecture. It establishes a foundation in microservices principles and core ML concepts before diving into practical aspects. You will learn how to design ML-specific microservices, implement them using frameworks like Flask, and containerize them with Docker for scalability. Data management strategies for ML are explored, including techniques for real-time data ingestion and data versioning. This book also addresses crucial aspects of securing ML microservices and using CI/CD practices to streamline development and deployment. Finally, you will discover real-world use cases showcasing how ML microservices are revolutionizing various industries, alongside a glimpse into the exciting future trends shaping this evolving field. Additionally, you will learn how to implement ML microservices with practical examples in Java and Python. This book merges software engineering and AI, guiding readers through modern development challenges. It is a guide for innovators, boosting efficiency and leading the way to a future of impactful technology solutions. WHAT YOU WILL LEARN ● Master the principles of microservices architecture for scalable software design. ● Deploy ML microservices using cloud platforms like AWS and Azure for scalability. ● Ensure ML microservices security with best practices in data encryption and access control. ● Utilize Docker and Kubernetes for efficient microservice containerization and orchestration. ● Implement CI/CD pipelines for automated, reliable ML model deployments. WHO THIS BOOK IS FOR This book is for data scientists, ML engineers, data engineers, DevOps team, and cloud engineers who are responsible for delivering real-time, accurate, and reliable ML models into production. TABLE OF CONTENTS 1. Introducing Microservices and Machine Learning 2. Foundation of Microservices 3. Fundamentals of Machine Learning 4. Designing Microservices for Machine Learning 5. Implementing Microservices for Machine Learning 6. Data Management in Machine Learning Microservices 7. Scaling and Load Balancing Machine Learning Microservices 8. Securing Machine Learning Microservices 9. Monitoring and Logging in Machine Learning Microservices 10. Deployment for Machine Learning Microservices 11. Real World Use Cases 12. Challenges and Future Trends

Python for Engineers

Author : Robert Johnson
Publisher : HiTeX Press
ISBN 13 :
Total Pages : 310 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Python for Engineers by : Robert Johnson

Download or read book Python for Engineers written by Robert Johnson and published by HiTeX Press. This book was released on 2024-10-25 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Python for Engineers: Solving Real-World Technical Challenges" is a comprehensive guide crafted for engineers who seek to leverage Python's capabilities in addressing complex technical problems. This meticulously structured book serves as a valuable resource for both beginners and seasoned programmers, offering clarity and depth across essential Python concepts that are pivotal in various engineering domains. From setting up the development environment to mastering core syntax and data types, each chapter builds on the previous, ensuring a well-rounded understanding of Python's robust capabilities. Delving deeper, the book covers advanced topics such as object-oriented programming, error handling, and the integration of powerful libraries and modules. Readers will gain practical insights into data handling, web development, and task automation, equipping them with the tools necessary for efficient and effective software development. By emphasizing both foundational skills and applied strategies, "Python for Engineers" empowers its readers to harness Python's potential, driving innovation and technical excellence in their engineering projects.

Big Data on Kubernetes

Author : Neylson Crepalde
Publisher : Packt Publishing Ltd
ISBN 13 : 1835468993
Total Pages : 297 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Big Data on Kubernetes by : Neylson Crepalde

Download or read book Big Data on Kubernetes written by Neylson Crepalde and published by Packt Publishing Ltd. This book was released on 2024-07-19 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience in building efficient and scalable big data architecture on Kubernetes, utilizing leading technologies such as Spark, Airflow, Kafka, and Trino Key Features Leverage Kubernetes in a cloud environment to integrate seamlessly with a variety of tools Explore best practices for optimizing the performance of big data pipelines Build end-to-end data pipelines and discover real-world use cases using popular tools like Spark, Airflow, and Kafka Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's data-driven world, organizations across different sectors need scalable and efficient solutions for processing large volumes of data. Kubernetes offers an open-source and cost-effective platform for deploying and managing big data tools and workloads, ensuring optimal resource utilization and minimizing operational overhead. If you want to master the art of building and deploying big data solutions using Kubernetes, then this book is for you. Written by an experienced data specialist, Big Data on Kubernetes takes you through the entire process of developing scalable and resilient data pipelines, with a focus on practical implementation. Starting with the basics, you’ll progress toward learning how to install Docker and run your first containerized applications. You’ll then explore Kubernetes architecture and understand its core components. This knowledge will pave the way for exploring a variety of essential tools for big data processing such as Apache Spark and Apache Airflow. You’ll also learn how to install and configure these tools on Kubernetes clusters. Throughout the book, you’ll gain hands-on experience building a complete big data stack on Kubernetes. By the end of this Kubernetes book, you’ll be equipped with the skills and knowledge you need to tackle real-world big data challenges with confidence.What you will learn Install and use Docker to run containers and build concise images Gain a deep understanding of Kubernetes architecture and its components Deploy and manage Kubernetes clusters on different cloud platforms Implement and manage data pipelines using Apache Spark and Apache Airflow Deploy and configure Apache Kafka for real-time data ingestion and processing Build and orchestrate a complete big data pipeline using open-source tools Deploy Generative AI applications on a Kubernetes-based architecture Who this book is for If you’re a data engineer, BI analyst, data team leader, data architect, or tech manager with a basic understanding of big data technologies, then this big data book is for you. Familiarity with the basics of Python programming, SQL queries, and YAML is required to understand the topics discussed in this book.

40 PYTHON LIBRARIES

Author : Diego Rodrigues
Publisher : Diego Rodrigues
ISBN 13 :
Total Pages : 363 pages
Book Rating : 4./5 ( download)

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Book Synopsis 40 PYTHON LIBRARIES by : Diego Rodrigues

Download or read book 40 PYTHON LIBRARIES written by Diego Rodrigues and published by Diego Rodrigues. This book was released on 2024-10-30 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: 🚀 TAKE ADVANTAGE OF THIS YEAR'S LAUNCH PROMOTIONAL PRICE 🚀 Become a Python expert with "40 PYTHON LIBRARIES: An Essential Guide for Students and Professionals - 2024 Edition", a must-have by Diego Rodrigues for students, developers, and professionals eager to master Python’s vast applications. Dive deep into Python's ecosystem and take your skills to the next level with essential libraries for scientific computing, data analysis, machine learning, web development, computer vision, and more. This guide simplifies complex concepts, showing you how to harness the power of NumPy, Pandas, Scikit-learn, Flask, Django, TensorFlow, PyTorch, and many others, to build robust applications and data solutions. Bring these libraries to life in practical projects—from large-scale data analysis and predictive modeling to web/mobile development, task automation, and integration with AWS, Google Cloud, and Microsoft Azure. With this book, you’ll develop the hands-on experience to tackle industry challenges and gain a competitive edge in the tech field. Boost your career with the knowledge to innovate and lead, mastering essential tools that will make your work more efficient and impactful. This practical, results-oriented book accelerates your learning and empowers your career. Order your copy today and start transforming your Python skills into a strategic advantage, turning complex challenges into effective solutions. TAGS: Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes